Digital Stylometry: Linking Profiles Across Social Networks

There is an ever growing number of users with accounts on multiple social media and networking sites. Consequently, there is increasing interest in matching user accounts and profiles across different social networks in order to create aggregate profiles of users. In this paper, we present models for Digital Stylometry, which is a method for matching users through stylometry inspired techniques. We experimented with linguistic, temporal, and combined temporal-linguistic models for matching user accounts, using standard and novel techniques. Using publicly available data, our best model, a combined temporal-linguistic one, was able to correctly match the accounts of 31% of 5,612 distinct users across Twitter and Facebook.Comment: SocInfo'15, Beijing, China. In proceedings of the 7th International Conference on Social Informatics (SocInfo 2015). Beijing, Chin

English Bards and Unknown Reviewers: a Stylometric Analysis of Thomas Moore and the Christabel Review

Fraught relations between authors and critics are a commonplace of literary history. The particular case that we discuss in this article, a negative review of Samuel Taylor Coleridge's Christabel (1816), has an additional point of interest beyond the usual mixture of amusement and resentment that surrounds a critical rebuke: the authorship of the review remains, to this day, uncertain. The purpose of this article is to investigate the possible candidacy of Thomas Moore as the author of the provocative review. It seeks to solve a puzzle of almost two hundred years, and in the process clear a valuable scholarly path in Irish Studies, Romanticism, and in our understanding of Moore's role in a prominent literary controversy of the age

Search Rank Fraud De-Anonymization in Online Systems

We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.Comment: The 29Th ACM Conference on Hypertext and Social Media, July 201